fix pylint line-too-long errors in tests

tests/test_diffeq/test_odefilter/test_approx_strategies/test_ek.py

@@ -45,7 +45,8 @@ def _setup(self):
 
     def test_call(self, fitzhughnagumo):
 
-        # not-yet-registered ODE raises errors as soon as the approximate info operator is called.
+        # not-yet-registered ODE raises errors as soon as the approximate
+        # info operator is called.
         with pytest.raises(ValueError):
             approx_transition = self.approx_strategy(self.info_op).as_transition()
             approx_transition.forward_realization(

tests/test_diffeq/test_odefilter/test_utils/test_problem_utils.py

@@ -92,8 +92,8 @@ def test_ivp_to_regression_problem(
 
     # If an approximation strategy is passed, the output should be an EKF component
     # which should suppoert forward_rv().
-    # If not, the output is a generic NonlinearGaussian (which has been tested for above.
-    # Recall that DiscreteEKFComponent implements NonlinearGaussian.)
+    # If not, the output is a generic NonlinearGaussian(which has been
+    # tested for above. Recall that DiscreteEKFComponent implements NonlinearGaussian.)
     if approx_strategy is not None:
         assert isinstance(
             regprob.measurement_models[1],

tests/test_linalg/test_solvers/test_belief_updates/test_matrix_based/test_matrix_based_linear_belief_update.py

@@ -93,7 +93,8 @@ def dense_matrix_based_update(
     np.testing.assert_allclose(
         updated_belief.A.cov.todense(),
         A_naive.cov.todense(),
-        err_msg="Covariance of system matrix estimate does not match naive implementation.",
+        err_msg="""Covariance of system matrix estimate does not
+        match naive implementation.""",
     )
 
     # Inverse
@@ -105,5 +106,6 @@ def dense_matrix_based_update(
     np.testing.assert_allclose(
         updated_belief.Ainv.cov.todense(),
         Ainv_naive.cov.todense(),
-        err_msg="Covariance of matrix inverse estimate does not match naive implementation.",
+        err_msg="""Covariance of matrix inverse estimate does not
+        match naive implementation.""",
     )

tests/test_linalg/test_solvers/test_belief_updates/test_matrix_based/test_symmetric_matrix_based_linear_belief_update.py

@@ -99,7 +99,8 @@ def dense_matrix_based_update(
     np.testing.assert_allclose(
         updated_belief.A.cov.todense(),
         A_naive.cov.todense(),
-        err_msg="Covariance of system matrix estimate does not match naive implementation.",
+        err_msg="""Covariance of system matrix estimate does not
+        match naive implementation.""",
     )
 
     # Inverse
@@ -111,5 +112,6 @@ def dense_matrix_based_update(
     np.testing.assert_allclose(
         updated_belief.Ainv.cov.todense(),
         Ainv_naive.cov.todense(),
-        err_msg="Covariance of matrix inverse estimate does not match naive implementation.",
+        err_msg="""Covariance of matrix inverse estimate does not
+        match naive implementation.""",
     )

tests/test_linops/test_arithmetics.py

@@ -238,7 +238,8 @@ def test_idkronecker_matmul():
     k1 = IdentityKronecker(4, np.random.rand(2, 3))  # (8, 12)
     k2 = IdentityKronecker(2, np.random.rand(6, 2))  # (12, 4)
 
-    # Even though the shapes fit, and IdentityKronecker @ IdentityKronecker = IdentityKronecker ....
+    # Even though the shapes fit, and
+    # IdentityKronecker @ IdentityKronecker = IdentityKronecker ....
     assert k1.shape[1] == k2.shape[0]
 
     # The result does not have a IdentityKronecker structure

tests/test_linops/test_arithmetics_fallbacks.py

@@ -3,7 +3,8 @@
 import numpy as np
 import pytest
 
-from probnum.linops._arithmetic_fallbacks import (  # NegatedLinearOperator,; ProductLinearOperator,; SumLinearOperator,
+from probnum.linops._arithmetic_fallbacks import (
+    # NegatedLinearOperator,; ProductLinearOperator,; SumLinearOperator,
     ScaledLinearOperator,
 )
 from probnum.linops._linear_operator import Matrix

tests/test_randvars/test_normal.py

@@ -138,7 +138,8 @@ def test_normal_dimension_mismatch(self):
             (np.array([[-1, 0], [2, 1]]), np.eye(3)),
         ]:
             with self.subTest():
-                err_msg = "Mean and kernels mismatch in normal distribution did not raise a ValueError."
+                err_msg = """Mean and kernels mismatch in normal distribution
+                did not raise a ValueError."""
                 with self.assertRaises(ValueError, msg=err_msg):
                     assert randvars.Normal(mean=mean, cov=cov)
 
@@ -204,9 +205,8 @@ def test_symmetric_samples(self):
                 B.T,
                 atol=1e-5,
                 rtol=1e-5,
-                msg="Sample {} from symmetric Kronecker distribution is not symmetric.".format(
-                    i
-                ),
+                msg=f"""Sample {i} from symmetric Kronecker
+                distribution is not symmetric.""",
             )
 
     def test_indexing(self):

tests/testing/statistics.py

@@ -14,18 +14,22 @@ def chi_squared_statistic(realisations, means, covs):
     with (assumed) means :math:`m_1, ..., m_N` and covariances
     :math:`C_1, ..., C_N`, compute the value
 
-    .. math:`\\chi^2 = \\frac{1}{Nd} \\sum_{n=1}^N (x_n - m_n)^\\top C_n^{-1}(x_n - m_n).`
+    .. math:`\\chi^2
+        = \\frac{1}{Nd} \\sum_{n=1}^N (x_n - m_n)^\\top C_n^{-1}(x_n - m_n).`
 
-    If it is roughly equal to 1, the samples are likely to correspond to given mean and covariance.
+    If it is roughly equal to 1, the samples are likely to correspond to given
+    mean and covariance.
 
     Parameters
     ----------
     realisations : array_like
         :math:`N` realisations of a :math:`d`-dimensional random variable. Shape (N, d).
     means : array_like
-        :math:`N`, :math:`d`-dimensional (assumed) means of a random variable. Shape (N, d).
+        :math:`N`, :math:`d`-dimensional (assumed) means of a random variable.
+        Shape (N, d).
     realisations : array_like
-        :math:`N`, :math:`d \\times d`-dimensional (assumed) covariances of a random variable. Shape (N, d, d).
+        :math:`N`, :math:`d \\times d`-dimensional (assumed) covariances of a random
+        variable. Shape (N, d, d).
     """
     if not realisations.shape == means.shape == covs.shape[:-1]:
         print(realisations.shape, means.shape, covs.shape)

tox.ini

@@ -80,4 +80,3 @@ commands =
     # pylint benchmarks --disable="unused-argument,attribute-defined-outside-init,missing-function-docstring" --jobs=0 # not a work in progress, but final
     pylint benchmarks --disable="unused-argument,attribute-defined-outside-init,no-else-return,no-self-use,consider-using-from-import,missing-module-docstring,missing-class-docstring,missing-function-docstring" --jobs=0
     # pylint tests --disable="missing-function-docstring" --jobs=0 # not a work in progress, but final
-    pylint tests --disable="arguments-differ,redefined-outer-name,too-many-instance-attributes,too-many-arguments,too-many-locals,too-few-public-methods,protected-access,unnecessary-pass,unused-variable,unused-argument,unused-private-member,attribute-defined-outside-init,no-else-return,no-self-use,consider-using-from-import,duplicate-code,line-too-long,missing-module-docstring,missing-class-docstring,missing-function-docstring,missing-param-doc,missing-type-doc,missing-raises-doc,missing-return-type-doc,redundant-returns-doc" --jobs=0